Use Batch Transform

Use batch transform when you need to do the following:

• Preprocess datasets to remove noise or bias that interferes with training or inference from your dataset.

• Get inferences from large datasets.

• Run inference when you don't need a persistent endpoint.

• Associate input records with inferences to assist the interpretation of results.

To filter input data before performing inferences or to associate input records with inferences about those records, see Associate Prediction Results with Input Records. For example, you can filter input data to provide context for creating and interpreting reports about the output data.

For more information about batch transforms, see Get Inferences for an Entire Dataset with Batch Transform.

Topics

• Use Batch Transform to Get Inferences from Large Datasets
• Speed up a Batch Transform Job
• Use Batch Transform to Test Production Variants
• Batch Transform Errors
• Batch Transform Sample Notebooks
• Associate Prediction Results with Input Records

Use Batch Transform to Get Inferences from Large Datasets

Batch transform automatically manages the processing of large datasets within the limits of specified parameters. For example, suppose that you have a dataset file, input1.csv, stored in an S3 bucket. The content of the input file might look like the following.:

Record1-Attribute1, Record1-Attribute2, Record1-Attribute3, ..., Record1-AttributeM
Record2-Attribute1, Record2-Attribute2, Record2-Attribute3, ..., Record2-AttributeM
Record3-Attribute1, Record3-Attribute2, Record3-Attribute3, ..., Record3-AttributeM
...
RecordN-Attribute1, RecordN-Attribute2, RecordN-Attribute3, ..., RecordN-AttributeM            

When a batch transform job starts, Amazon SageMaker initializes compute instances and distributes the inference or preprocessing workload between them. When you have multiples files, one instance might process input1.csv, and another instance might process the file named input2.csv.

To keep large payloads below the MaxPayloadInMB limit, you can split an input file into several mini-batches. For example, you might create a mini-batch from input1.csv by including only two of the records.

Record3-Attribute1, Record3-Attribute2, Record3-Attribute3, ..., Record3-AttributeM
Record4-Attribute1, Record4-Attribute2, Record4-Attribute3, ..., Record4-AttributeM
            

Note

Amazon SageMaker processes each input file separately. It doesn't combine mini-batches from different input files to comply with the MaxPayloadInMB limit.

To split input files into mini-batches, when you create a batch transform job, set the SplitType parameter value to Line. If SplitType is set to None or if an input file can't be split into mini-batches, Amazon SageMaker uses the entire input file in a single request.

If the batch transform job successfully processes all of the records in an input file, it creates an output file with the same name and the .out file extension. For multiple input files, such as input1.csv and input2.csv, the output files are named input1.csv.out and input2.csv.out. The batch transform job stores the output files in the specified location in Amazon S3, such as s3://awsexamplebucket/output/.

The predictions in an output file are listed in the same order as the corresponding records in the input file. The output file input1.csv.out, based on the input file shown earlier, would look like the following.

Inference1-Attribute1, Inference1-Attribute2, Inference1-Attribute3, ..., Inference1-AttributeM
Inference2-Attribute1, Inference2-Attribute2, Inference2-Attribute3, ..., Inference2-AttributeM
Inference3-Attribute1, Inference3-Attribute2, Inference3-Attribute3, ..., Inference3-AttributeM
...
InferenceN-Attribute1, Inference3-Attribute2, Inference3-Attribute3, ..., InferenceN-AttributeM            

To combine the results of multiple output files into a single output file, set the AssembleWith parameter to Line.

When the input data is very large and is transmitted using HTTP chunked encoding, to stream the data to the algorithm, set MaxPayloadInMB to 0. Amazon SageMaker built-in algorithms don't support this feature.

For information about using the API to create a batch transform job, see the CreateTransformJob API. For more information about the correlation between batch transform input and output objects, see OutputDataConfig. For an example of how to use batch transform, see Step 6.2: Deploy the Model with Batch Transform.

Speed up a Batch Transform Job

If you are using the CreateTransformJob API, you can reduce the time it takes to complete batch transform jobs by using optimal values for parameters such as MaxPayloadInMB, MaxConcurrentTransforms, or BatchStrategy. If you are using the Amazon SageMaker console, you can specify these optimal parameter values in the Additional configuration section of the Batch transform job configuration page. Amazon SageMaker automatically finds the optimal parameter settings for built-in algorithms. For custom algorithms, provide these values through an execution-parameters endpoint.

Use Batch Transform to Test Production Variants

To test different models or various hyperparameter settings, create a separate transform job for each new model variant and use a validation dataset. For each transform job, specify a unique model name and location in Amazon S3 for the output file. To analyze the results, use Inference Pipeline Logs and Metrics.

Batch Transform Errors

Amazon SageMaker uses the Amazon S3 Multipart Upload API to upload results from a batch transform job to Amazon S3. If an error occurs, the uploaded results are removed from Amazon S3. In some cases, such as when a network outage occurs, an incomplete multipart upload might remain in Amazon S3. To avoid incurring storage charges, we recommend that you add the S3 bucket policy to the S3 bucket lifecycle rules. This policy deletes incomplete multipart uploads that might be stored in the S3 bucket. For more information, see Object Lifecycle Management.

If a batch transform job fails to process an input file because of a problem with the dataset, Amazon SageMaker marks the job as failed. If an input file contains a bad record, the transform job doesn't create an output file for that input file because doing so prevents it from maintaining the same order in the transformed data as in the input file. When your dataset has multiple input files, a transform job continues to process input files even if it fails to process one. The processed files still generate useable results.

Exceeding the MaxPayloadInMB limit causes an error. This might happen with a large dataset if it can't be split, the SplitType parameter is set to none, or individual records within the dataset exceed the limit.

If you are using your own algorithms, you can use placeholder text, such as ERROR, when the algorithm finds a bad record in an input file. For example, if the last record in a dataset is bad, the algorithm places the placeholder text for that record in the output file.

Batch Transform Sample Notebooks

For a sample notebook that uses batch transform with a principal component analysis (PCA) model to reduce data in a user-item review matrix, followed by the application of a density-based spatial clustering of applications with noise (DBSCAN) algorithm to cluster movies, see Batch Transform with PCA and DBSCAN Movie Clusters. For instructions on creating and accessing Jupyter notebook instances that you can use to run the example in Amazon SageMaker, see Use Amazon SageMaker Notebook Instances. After creating and opening a notebook instance, choose the SageMaker Examples tab to see a list of all the Amazon SageMaker examples. The topic modeling example notebooks that use the NTM algorithms are located in the Advanced functionality section. To open a notebook, choose its Use tab, then choose Create copy.